Update app.py

app.py

@@ -1,44 +1,66 @@
-# chatbot_url_query.py
+# app.py
 
 import os
+from dotenv import load_dotenv
+from tavily import TavilyClient
 from langchain_google_genai import ChatGoogleGenerativeAI
-from langchain.retrievers import TavilyRetriever
-from langchain.chains import RetrievalQA
+from langchain.chains import LLMChain
+from langchain.prompts import PromptTemplate
 
-# 1. Set API keys (Replace with your actual keys)
-os.environ["GOOGLE_API_KEY"] = "your-google-api-key"
-os.environ["TAVILY_API_KEY"] = "your-tavily-api-key"
+# 1. Load environment variables from .env file (if present)
+load_dotenv()
 
-# 2. Load Google Gemini model
+# 2. Set API keys from environment
+GOOGLE_API_KEY = os.environ.get("GOOGLE_API_KEY")
+TAVILY_API_KEY = os.environ.get("TAVILY_API_KEY")
+
+# 3. Initialize Gemini LLM
 llm = ChatGoogleGenerativeAI(
     model="models/gemini-1.5-flash",
-    google_api_key=os.environ["GOOGLE_API_KEY"]
+    google_api_key=GOOGLE_API_KEY
 )
 
-# 3. Initialize Tavily Retriever (fetches data from web)
-retriever = TavilyRetriever(k=3)
+# 4. Tavily Client
+tavily_client = TavilyClient(api_key=TAVILY_API_KEY)
+
+def extract_website_text(url):
+    result = tavily_client.extract(urls=url)
+    if result and "text" in result[0]:
+        return result[0]["text"]
+    return "Could not extract content from the URL."
+
+# 5. Prompt Template
+prompt = PromptTemplate(
+    input_variables=["website_content", "question"],
+    template="""
+You are an intelligent assistant. Based on the following website content:
+
+{website_content}
 
-# 4. Create Retrieval QA Chain
-qa_chain = RetrievalQA.from_chain_type(
-    llm=llm,
-    retriever=retriever,
-    return_source_documents=True
+Answer the following question:
+{question}
+"""
 )
 
-# 5. Function to run the chatbot
-def ask_web_query(url, question):
-    query = f"Given the content of the website {url}, answer the following: {question}"
-    response = qa_chain.invoke({"query": query})
+# 6. LLM QA Chain
+qa_chain = LLMChain(llm=llm, prompt=prompt)
+
+# 7. Main Chat Function
+def ask_from_website(url, question):
+    print(f"\n🔗 Extracting content from: {url}")
+    website_text = extract_website_text(url)
     
-    print("\n✅ Answer:")
-    print(response["result"])
+    print(f"\n💬 Asking: {question}")
+    response = qa_chain.invoke({
+        "website_content": website_text,
+        "question": question
+    })
 
-    print("\n📄 Sources:")
-    for doc in response["source_documents"]:
-        print("-", doc.metadata.get("source", "Unknown"))
+    print("\n✅ Answer:")
+    print(response["text"])
 
-# 6. Run your chatbot
+# 8. Run
 if __name__ == "__main__":
-    url = input("Enter website URL: ")
-    question = input("Ask your question about the website: ")
-    ask_web_query(url, question)
+    url = input("Enter the website URL: ")
+    question = input("What do you want to ask about this website? ")
+    ask_from_website(url, question)